class_docstr: We'll use this mock dataset reader for most of the tests.
  
  It utilizes a transformers tokenizer, since historically we've seen deadlocking
  issues when using these within subprocesses. So these tests also serve as
  regression tests against those issues.
  
  And unlike `MockOldDatasetReader` below, it implements all of the new API,
  specifically the `apply_token_indexers` method, so that it can be used
  with num_workers > 0.
code: def _read(self, file_path: str):
      for i in self.shard_iterable(range(self.NUM_INSTANCES)):
          source = f"Hi there, I'm the {i}th instance"
          target = f"Hello, {i}th instance!"
          yield self.text_to_instance(i, source, target)
  
imports: from typing import List, Iterable, Dict
  import torch
  import pytest
  from allennlp.common.testing import requires_gpu
  from allennlp.data.instance import Instance
  from allennlp.data.dataset_readers import DatasetReader
  from allennlp.data.data_loaders import MultiProcessDataLoader, WorkerError
  from allennlp.data.fields import Field, TextField, MetadataField, TensorField
  from allennlp.data.tokenizers import PretrainedTransformerTokenizer
  from allennlp.data.token_indexers import PretrainedTransformerIndexer
  from allennlp.data.vocabulary import Vocabulary
  from allennlp.data.data_loaders.data_collator import LanguageModelingDataCollator
label_desc: Use image node_id 2 for calling the MockDatasetReader obj's underlying member method code with example usage: obj._read(file_path) without return types